气象干旱对青海牧草产量的影响及其在产量预报中的应用

研究了降水偏少造成的气象干旱对青海省牧草产量的影响,得出牧草生长季降水与产量的关系模式,计算出气象干旱造成的牧草产量损失量。但该方法模拟误差较大,且不能满足牧草产量预报、草场载畜量确定和牲畜出栏率确定等牧业生产服务需求。因此,根据拉格朗日插值方法给出了青海省无干旱时“未受旱产量”的确定方法,并据此求算出历年干旱对青海省牧草产量的损失量值,然后依据天然牧草不同生育期发生干旱的强度、范围以及牧草对干旱的敏感度等关系,建立了干旱损失量的统计和评估模式。在2013-2015年青海省曲麻莱县牧草产量评估中进行了应用,3年的误差在3.59%～6.02％之间,证明了该方法可以评估出研究区干旱对牧草产量的损失量,从而评估出实际牧草产量。拉格朗日插值方法效果较好,可以在青海省今后的牧草产量预报、草场载畜量预报、牲畜出栏率确定等畜牧业生产评估中推广应用。     

基于大范围地面墒情监测的鄱阳湖流域农业干旱

以鄱阳湖流域为研究区, 基于2011—2020年22个墒情站的逐日地面墒情监测数据、1956—2020年49个雨量站的日降雨数据及2016—2019年墒情站所在灌区的气象数据, 采用考虑植被生理状态的土壤水分亏缺指数(SWDI)表征农业干旱, 分析不同尺度下墒情、包气带缺水量和降水量的时空分布, 评估SWDI在鄱阳湖流域农业干旱监测中的适用性, 揭示该流域农业干旱时空演变特征及其对气象干旱的响应规律, 初步探讨土壤质地与农业干旱强度的相关性。结果表明: ① SWDI对鄱阳湖流域农业干旱诊断具有较好的适用性; ②近10 a该流域农业干旱呈显著加重趋势, 其中2019—2020年发生流域性重度农业干旱, 且夏、秋、冬连旱, 是近10 a的主导季节性农业干旱, 对水稻、油菜等粮食产量影响显著; ③相较于气象干旱, 农业干旱发生、结束时间分别平均约晚2.5周和3周, 历时长10.1周, 频次更低, 干旱等级更小; ④砂土持水性最差, 易发生特大农业干旱, 黏土、黏壤土保水性最好, 轻旱和中旱发生概率较大, 壤土、砂壤土和壤砂土则介于二者之间。     

灌区干旱风险评估模型研究

根据风险理论,建立了包括农业干旱发生概率、抗旱能力、受灾体种植面积比等多因子的灌区农业干旱风险评估模型。并将相对产量作为灌区农业干旱评估指标,能够反映土壤 作物 大气系统中水分运动对农业生产的影响,利用该指标结合干旱风险评估模型对灌区农业干旱进行风险评估,分析出灌区各种作物对干旱风险度影响最大的生育阶段和风险度最高的农作物,以便灌区制定合理的抗旱方案以减小灌区干旱损失。     

青海省气象灾害的若干气候特征分析

利用1984-2007年气象灾害资料, 分析了青海省各类气象灾害的发生机率及时空分布特征.冰雹、 暴雨洪涝、 雪灾、 干旱和雷电及霜冻是青海省主要气象灾害, 连阴雨、 大风、 沙尘暴、 冻害、 渍涝、 高温、 龙卷风、 大雾为青海次要气象灾害.在地域分布上, 海南州和海东地区是青海气象灾害多发和危害严重地区; 玉树州和果洛州属于气象灾害发生较少和危害较轻地区.就行政县域而言, 西宁市湟中县、 海南州兴海县、 贵德县为青海气象灾害多发县, 果洛州班玛县、 久治县、 玉树州治多县则是青海省气象灾害较少和危害较轻县份.年内5-6月出现的气象灾害种类最多, 4-9月份是气象灾害高发期.在1984-2007年间, 暴雨洪涝、 冰雹、 雷电灾害发生次数呈增多趋势, 其他灾害发生次数增多趋势不甚明显.     

青海牧区干旱、 雪灾灾害损失综合评估技术研究

利用青海省20个草地生态监测站点2003-2011年监测的牧草、 降水和相应年份的灾害监测调查资料分析表明, 青海高原草地牧草产量与降水相关密切, 8月末牧草产量与5-8月降水量相关系数均达到了0.001的相关性检验, 建立了不同草地类型不同月份牧草产量与降水量的最优模拟方程; 利用降水量距平百分率划分干旱等级, 建立了干旱等级与牧草和牲畜损失的对应关系, 实现了干旱和雪灾对草地、 牲畜直接经济损失的定量评估. 对比分析了干旱和雪灾灾害对畜牧业造成损失比例, 将干旱和雪灾两个不同概念的灾害通过经济损失有机地联系起来, 直观地表明了在不同等级的灾害情况下, 损失造成的大小和比例. 结果表明: 随着灾害等级加重, 雪灾造成的损失比例增大, 在特大灾时, 雪灾造成的损失是旱灾损失的2.5倍.评估模型符合草地牧草生长规律和畜牧业生产特征, 实例评估符合畜牧业实际损失程度. 因此, 能够在降水偏少或偏多时对草地畜牧业评估服务中推广应用.     

基于灾损评估的青海省牧草干旱风险区划研究

在全球气候变暖的大背景下, 表现出温度升高、 降水变率加大的区域响应, 造成极端天气气候事件、 气象灾害加剧. 基于青海省1961-2010年47个气象站和20个农气站的气象资料、 牧草的实际产量以及牧草的理论产量等资料, 采用相关分析、 线性回归等方法, 在分析致灾因子危险性、 牧草相对产量的基础上, 确定了青海省牧区牧草干旱风险评估的实际阈值.通过海拔、 经度、 纬度、 牧草旱灾发生频次的拟合方程, 结合GIS平台对青海省牧草干旱进行风险区划.结果表明: 青南牧区西部、 环青海湖地区、 柴达木盆地东部边缘地区、 祁连山地区为易受旱灾影响的特高风险或高风险区域;青南牧区西南部为中风险区域;低风险区域主要在青南牧区东南部, 区划结果基本上于历史旱灾的实际情况相吻合, 区划结果旨在为青海省牧区牧业良性发展提供科学依据.     

气象极值变化及其导致的农业气象灾害趋势分析——以甘肃省为例

依据甘肃省62个气象站1960-2005年的地面气候资料, 分析农作物生育期(3-10月)内气象要素极值的变化趋势, 并用主成分分析方法研究了该变化对农业气象灾害发生趋势的影响.结果表明: 与温度相关的气象要素极值表现出了同平均气温类似的显著上升趋势,其中降水相关的气象要素极值变化说明甘肃省降水结构发生了改变, 极端性有所减弱;其他相关要素中, 最大风速存在明显下降趋势, 蒸发量呈下降趋势、日照时数呈上升趋势, 但都不够显著.大部分气象极值的变化与农业气象灾害的变化呈正相联系, 只有很少部分气象极值的变化与农业气象灾害的变化呈反相联系.综合分析这些气象极值在甘肃省范围内的变化趋势, 揭示出未来甘肃省干旱灾害倾向于加剧, 而洪涝灾害趋于减轻.     

两种判断月干旱过程的方法在黄河流域的对比研究

利用黄河流域160个气象站1961-2010年逐日综合气象干旱指数(CI)指数, 对比分析两种用逐日CI指数判断月干旱过程的方法. 结果表明: 对重大干旱事件个例来说, 两种方法都能大体描述事件的月干旱过程, 但干旱的强度和范围有所不同. 从干旱发生的范围来看, 两种结果的差别较小, 方法I的识别结果范围更大、更连续, 特别是对青海旱情的判断常常比实际范围大;而方法II的识别结果范围稍小, 大体上能反应干旱的整体范围, 但有时也偶尔会遗漏小部分旱区;从干旱发生的强度来看, 方法I对干旱事实的描述偏轻, 而方法II以重-特旱为主, 与实际情况更相符. 从对黄河流域近50 a月干旱频率的分析结果来看, 两种方法一致表明黄河流域分界线以西的地区常年不容易发生干旱, 而对于分界线以东地区, 两种方法的统计结果有较大差异. 方法I的结果表明, 分界线以东地区干旱的月发生频率较大, 其中, 轻旱的月发生频率最大, 其次为中旱, 而重旱和特旱的发生频率很小;方法II的结果表明, 分界线以东地区干旱的月发生频率在60%~80%左右, 其中重旱的月发生频率最大, 其次为中旱, 轻旱和特旱的发生频率很小. 总体来说, 方法II对黄河流域月干旱情况的评估结果与干旱实际情况更一致.     

青藏高原地区气象干旱研究进展与展望

青藏高原是承受自然灾害脆弱性较高的地区。该区域经常遭受雪灾、干旱、大风、雷电、冰雹和洪涝等气象灾害的危害,其中,干旱是该区域除雪灾外影响最为严重的气象灾害。随着气候变化和人类活动的加剧,青藏高原由气象灾害造成的损失不断加剧。为此,着眼于青藏高原的区域特点,对其气象干旱研究现状进行了梳理与分析,系统总结了青藏高原气象干旱的主要研究成果,揭示了青藏高原气象干旱时空分布的基本特征：干旱的高发区在高原的北部、东北部、西南部和东南部,高发时段为1980年代和2000年代;归纳了青藏高原气象干旱监测和预测的主要技术方法：基于干旱指数开展的干旱监测评估和基于干旱影响因子利用气候模式进行的干旱预测;给出了青藏高原干旱灾害风险的发生规律：青藏高原东北部偏南地区是农牧业干旱灾害的高风险区,东北部、西南部和东南部是较高风险区;高寒草原比高寒草甸面临的干旱灾害风险高。基于数值模式和未来情景,预估21世纪青藏高原气温升高、降水增加;但由于降水增加表现出明显的时空分布不均匀性,未来发生季节性和区域性气象干旱的可能性仍然很大;同时,提出了青藏高原气象干旱研究在资料、技术方法和模式应用等方面存在的问题,并结合国际前沿...     

基于CI指数的甘肃省黄土高原地区气象干旱的变化趋势分析

基于甘肃省黄土高原区33个气象站1962-2010年气象资料, 利用综合气象干旱指数(CI)对其近50 a的干旱频率和平均持续时间的空间分布、 干旱强度趋势变化和极端干旱事件频次进行了分析, 此基础上应用基于分型理论的R/S方法对干旱强度未来变化趋势进行了预测. 结果表明: 甘肃省黄土高原区干旱发生频率和多年平均持续天数在兰州-靖远一带和庆阳北部属于高值区, 而岷县、 渭源一带属于低值区; 106° E以西"临洮-通渭-天水"一带和庆阳东南部是干旱变幅最大的地方. 20世纪90年代以来, 干旱强度增大的较快, 四季均呈现出干旱强度变大的趋势, 其中春、 秋季干旱强度加剧的趋势明显, 夏季近10 a都处于非常严重的干旱状态, 但未通过0.01的显著性检验; 20世纪60年代至今, 极端干旱事件发生频次快速增多. 四季干旱强度Hurst指数H 均大于0.5, 同时分维数D 均小于1.5, 因此, 未来一段时间干旱强度仍然保持与过去相一致的变化趋势. 研究结果可为相关部门制定相应抗旱对策提供科学依据.     

Assessment on agricultural drought vulnerability in the Yellow River basin based on a fuzzy clustering iterative model

Drought is one of the major natural disasters occurring in China and causes severe impacts on agricultural production and food security. Therefore, agricultural drought vulnerability assessment has an important significance for reducing regional agricultural drought losses and drought disaster risks. In view of agricultural drought vulnerability assessment with the characteristics of multiple factors and uncertainty, we applied the fuzzy comprehensive evaluation framework to agricultural drought vulnerability model. The agricultural drought vulnerability assessment model was constructed based on the multi-layer and multi-index fuzzy clustering iterative method, which can better reveal the drought vulnerability (including sensitivity and adaptation capacity). Furthermore, the cycle iterative algorithm was used to obtain the optimal index weight vector of a given accuracy by setting the objective function. It provides a new approach to weight determination of agricultural drought vulnerability assessment. In this study, agricultural drought vulnerability of 65 cities (as well as leagues and states) in the Yellow River basin was investigated using a fuzzy clustering iterative model and visualized by using GIS technique. The results showed clear differences and regularities among the spatial distribution of agricultural drought vulnerability of different regions. A large number of the regions in the basin consisted of those exhibiting high to very high vulnerability and were mainly distributed throughout Qinghai, Gansu, northern Shaanxi, and southern Shanxi, accounting for 46 % of the total assessment units. However, the regions exhibiting very high vulnerability were not significantly affected by droughts. Most of the regions exhibiting moderate vulnerability (21.5 % of the assessment units) were mainly concentrated among agricultural irrigation areas, where agriculture is highly sensitive to droughts, and drought occurrence in these regions will likely cause heavy losses in the future. The regions exhibiting slight to low vulnerability were relatively concentrated, accounting for 32.3 % of the assessment units, and were mainly distributed in the plains of the lower reaches of the Yellow River, where the economy was rather well developed and the agricultural production conditions were relatively stronger.     

农业干旱灾害风险模糊集对评价法及其应用

开展农业干旱灾害风险评估,有利于定量认识农业旱灾和科学指导防旱抗旱工作。基于集对分析原理和模糊理论建立的模糊集对评价法,兼顾了信息的多尺度特征和评价等级的模糊性,概念清晰,计算简洁。构建了由旱灾危险性子系统、旱灾暴露性子系统、灾损敏感性子系统和抗旱能力子系统组成的干旱灾害风险评估体系和评价指标。将模糊集对评价法应用于2012年安徽省亳州市农业干旱灾害风险评估,研究结果表明,建议方法是可靠的,为农业干旱灾害风险评估提供了一种新途径。     

Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model

Due to the complex characteristics of drought, drought risk needs to be quantified by combining drought vulnerability and drought hazard. Recently, the major focus in drought vulnerability has been on how to calculate the weights of indicators to comprehensively quantify drought risk. In this study, principal component analysis (PCA), a Gaussian mixture model (GMM), and the equal-weighting method (EWM) were applied to objectively determine the weights for drought vulnerability assessment in Chungcheong Province, located in the west-central part of South Korea. The PCA provided larger weights for agricultural and industrial factors, whereas the GMM computed larger weights for agricultural factors than did the EWM. The drought risk was assessed by combining the drought vulnerability index (DVI) and the drought hazard index (DHI). Based on the DVI, the most vulnerable region was CCN9 in the northwestern part of the province, whereas the most drought-prone region based on the DHI was CCN12 in the southwest. Considering both DVI and DHI, the regions with the highest risk were CCN12 and CCN10 in the southern part of the province. Using the proposed PCA and GMM, we validated drought vulnerability using objective weighting methods and assessed comprehensive drought risk considering both meteorological hazard and socioeconomic vulnerability.

     

青海省东部农业区雷电灾害风险评估

利用2005-2013年青海省东部农业区14个县的雷电、雷电灾害、人口数量和生产总值等资料,采用《雷电灾害风险评估技术规范》（DB 50/214-2006）中的雷电灾害易损性风险评估指标,分析了该地的雷电灾害风险及区划,该风险区划对青海省东北农业区防御雷电灾害规划具有很好的参考价值,也是制定科学合理的雷电防护技术路线的重要依据：青海省东部农业区9年间共出现雷电3 801 d,最早出现在3月下旬,最晚结束在11月上旬,雷电出现最多地区为大通县;雷电灾害共出现47次,最早出现在4月,10月份结束,发生最多在6月份,盛夏7月雷电灾害却少,西宁市和湟源县雷电灾害出现最多,湟中县雷电造成的人员伤亡最多,共伤亡11人,大部分在山间劳作的村民和牧民,与青海省东部农业区发生的其它气象灾害相比,雷电灾害最容易造成人员和牲畜伤亡。雷电灾害风险以西宁市为中心,向四周扩散逐渐降低,北部地区相对高于南部地区,西宁市、大通县遭受雷电灾害的可能性程度最大,乐都县虽然近9年没有上报的雷电灾情,但它的雷电灾害风险并不低。     

Immediate impacts of the Wenchuan Earthquake on the prices and productions of grain and pork products

Based on field survey and statistical analysis, the immediate impacts of the Wenchuan Earthquake happened on May 12, 2008, are evaluated from two aspects, i.e., the influence on the prices and yields of grain and pork products in the local region and that on China. Wenchuan Earthquake, undoubtedly, has some negative effects on the local agricultural yields in Sichuan Province. It has caused immediate impacts on the grain and livestock breeding industry in the earthquake worst-hit counties. For example, due to the earthquake, the grain yields in Sichuan Province will decreased more than 0.4% and the pork productions decrease 5% at least. Thus, prices of grain and pork products are likely to rise in local mountainous areas over a short term. Our studying results that the disaster rate, the hazard rate and the complete loss rate of grain productions are 18%, 10% and 6% respectively in the earthquake worst-hit counties, while the disaster rate in the eastern plain areas even reach to 30%-49%. Even so, the results of model analysis for sample survey indicates that Wenchuan Earthquake has caused only marginal impacts on agricultural production, does not heavily hurt the stability of the prices and yields of grain and pork products at the national level. In other words,Wenchuan Earthquake had not affected the overall situation of national agricultural production. It is estimated that the reduction rate of national grain yield is as low as 0.006%, and the price changes of grain and pork products are no more than 0.5% and 2.2% respectively.     

The impact of irrigation water supply rate on agricultural drought disaster risk: a case about maize based on EPIC in Baicheng City,China

This study presents a methodology for risk analysis, assessment, and combination of drought disasters under the different irrigational levels in Baicheng City, which is supported by run theory, copula functions, crop growth model, and technique of natural disaster risk assessment from the viewpoints of climatology, geography, hydrology, agricultural science, disaster science, environmental science, and so on. Along with the global warming, the occurrences of water-related disasters become more frequent and more serious. It is necessary to determine the laws of the relationship between irrigational ability and the loss caused by drought. Drought events were identified by using run theory; the drought frequency was calculated by using copula function; the loss of every drought event was simulated by using EPIC model; and the relationship curves under the different irrigational supply conditions between the drought frequency and the yield reduction rate of the drought event were fitted to assess the impact of irrigational supply rate on the loss caused by drought. The results show that in the range of crop water demand, the loss caused by drought decreases as the result of the increase in irrigational supply rate; however, their variations are not proportional. The loss caused by the certain frequency drought event under the certain irrigational supply condition could be calculated by the curve of drought disaster risk assessment constructed by this study. The results obtained from this study are specifically intended to support local and national governmental agencies on agricultural disaster management.